tfx.components.ExampleValidator

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A TFX component to validate input examples.

Inherits From: BaseComponent

tfx.components.ExampleValidator(
    statistics: tfx.types.Channel = None,
    schema: tfx.types.Channel = None,
    output: Optional[tfx.types.Channel] = None,
    stats: Optional[tfx.types.Channel] = None,
    instance_name: Optional[Text] = None
)

Used in the notebooks

Used in the tutorials

The ExampleValidator component uses Tensorflow Data Validation to validate the statistics of some splits on input examples against a schema.

The ExampleValidator component identifies anomalies in training and serving data. The component can be configured to detect different classes of anomalies in the data. It can:

  • perform validity checks by comparing data statistics against a schema that codifies expectations of the user.
  • detect data drift by looking at a series of data.
  • detect changes in dataset-wide data (i.e., num_examples) across spans or versions.

Schema Based Example Validation The ExampleValidator component identifies any anomalies in the example data by comparing data statistics computed by the StatisticsGen component against a schema. The schema codifies properties which the input data is expected to satisfy, and is provided and maintained by the user.

Please see https://www.tensorflow.org/tfx/data_validation for more details.

Example

# Performs anomaly detection based on statistics and data schema.
validate_stats = ExampleValidator(
    statistics=statistics_gen.outputs['statistics'],
    schema=infer_schema.outputs['schema'])

statistics A Channel of type standard_artifacts.ExampleStatistics. This should contain at least 'eval' split. Other splits are currently ignored.
schema A Channel of type standard_artifacts.Schema. required
output Output channel of type standard_artifacts.ExampleAnomalies.
stats Backwards compatibility alias for the 'statistics' argument.
instance_name Optional name assigned to this specific instance of ExampleValidator. Required only if multiple ExampleValidator components are declared in the same pipeline. Either stats or statistics must be present in the arguments.

component_id DEPRECATED FUNCTION
component_type DEPRECATED FUNCTION
downstream_nodes

exec_properties

id Node id, unique across all TFX nodes in a pipeline.

If instance name is available, node_id will be: . otherwise, node_id will be:

inputs

outputs

type

upstream_nodes

Child Classes

class DRIVER_CLASS

class SPEC_CLASS

Methods

add_downstream_node

View source

add_downstream_node(
    downstream_node
)

Experimental: Add another component that must run after this one.

This method enables task-based dependencies by enforcing execution order for synchronous pipelines on supported platforms. Currently, the supported platforms are Airflow, Beam, and Kubeflow Pipelines.

Note that this API call should be considered experimental, and may not work with asynchronous pipelines, sub-pipelines and pipelines with conditional nodes. We also recommend relying on data for capturing dependencies where possible to ensure data lineage is fully captured within MLMD.

It is symmetric with add_upstream_node.

Args
downstream_node a component that must run after this node.

add_upstream_node

View source

add_upstream_node(
    upstream_node
)

Experimental: Add another component that must run before this one.

This method enables task-based dependencies by enforcing execution order for synchronous pipelines on supported platforms. Currently, the supported platforms are Airflow, Beam, and Kubeflow Pipelines.

Note that this API call should be considered experimental, and may not work with asynchronous pipelines, sub-pipelines and pipelines with conditional nodes. We also recommend relying on data for capturing dependencies where possible to ensure data lineage is fully captured within MLMD.

It is symmetric with add_downstream_node.

Args
upstream_node a component that must run before this node.

from_json_dict

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@classmethod
from_json_dict(
    dict_data: Dict[Text, Any]
) -> Any

Convert from dictionary data to an object.

get_id

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@classmethod
get_id(
    instance_name: Optional[Text] = None
)

Gets the id of a node.

This can be used during pipeline authoring time. For example: from tfx.components import Trainer

resolver = ResolverNode(..., model=Channel( type=Model, producer_component_id=Trainer.get_id('my_trainer')))

Args
instance_name (Optional) instance name of a node. If given, the instance name will be taken into consideration when generating the id.

Returns
an id for the node.

to_json_dict

View source

to_json_dict() -> Dict[Text, Any]

Convert from an object to a JSON serializable dictionary.

Class Variables

  • EXECUTOR_SPEC